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摘要:
针对弹道中段目标高分辨距离像(HRRP)的时序特征提取和识别问题，为充分利用弹道中段目标HRRP的双向时序信息，进一步提高识别性能，提出一种基于加性融合双向时间卷积神经网络(AF-BiTCN)的识别方法。对HRRP数据采用双向时序滑窗法处理为双向序列；构建BiTCN逐层提取HRRP的双向深层时序特征，并将双向时序特征采用加性策略融合；利用更加稳健的融合特征实现对弹道中段目标的识别，并使用Adam算法优化AF-BiTCN的收敛速度和稳定性。实验结果表明：所提的基于AF-BiTCN的弹道中段目标HRRP识别方法较堆叠选择长短期记忆网络(SLSTM)、堆叠门控循环单元(SGRU)等6种时序方法具有更高的准确率和更快的识别速度，在测试集上达到了96.60%的准确率，并且在噪声数据集上表现出更好的鲁棒性。
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关键词:
- 双向时间卷积神经网络 /
- 弹道目标识别 /
- 特征融合 /
- 高分辨距离像 /
- 滑窗算法
Abstract:To address the problem of temporal feature extraction and recognition of high-resolution range profiles (HRRP) of midcourse ballistic targets, a recognition method based on bidirectional temporal convolutional networks with additive fusion (AF-BiTCN) was proposed, which could make full use of the bidirectional temporal information of HRRP of midcourse ballistic targets and further improve the recognition performance. Firstly, the HRRP data was processed into a bidirectional sequence by the bidirectional sliding window algorithm. Then, the BiTCN was constructed to extract bidirectional deep temporal features of HRRP in each layer, and the bidirectional features were fused by an additive strategy. Finally, more robust fusion features were utilized to recognize ballistic targets, and the Adam algorithm was used to optimize the convergence speed and stability of AF-BiTCN. The experimental results show that the proposed HRRP recognition method of midcourse ballistic targets based on AF-BiTCN in this paper has higher accuracy and faster recognition speed compared with six methods such as stack long short-term memory (SLSTM), stack gate recurrent unit (SGRU) and so on, and it achieves an accuracy of 96.60% on the test set. Moreover, the proposed method indicates better robustness on noise datasets.
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图 6 AF-BiTCN参数配置实验结果
Figure 6. Results of AF-BiTCN parameter configuration experiments
表 1 AF-BiTCN参数设置
Table 1. AF-BiTCN parameters setting
参数 数值 损失函数 交叉熵 优化器 Adam[24] 批量大小 256 学习率 0.000 3 一阶矩衰减率 0.9 二阶矩衰减率 0.999 滑窗窗长 32 滑窗间隔 16 迭代次数 400 卷积核 256,128,64,64 卷积核尺寸 1×3 膨胀系数 1,2,8,16 Dropout率 0.5 
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表 2 不同方法性能对比
Table 2. Performance comparison of different methods
模型 准确率/% 参数量 识别速度/μs AF-BiTCN 96.60 1.16×106 334 CNN-BiGRU 95.78 1.76×106 792 Attention-BiGRU 95.57 1.15×106 708 BiGRU 95.37 1.15×106 670 BiLSTM 94.70 1.52×106 884 SGRU 94.17 0.56×106 374 SLSTM 93.87 0.58×106 392
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表 3 不同信噪比条件下鲁棒性对比实验的识别准确率
Table 3. Recognition accuracy of robustness comparison experiments under different signal-to-noise ratios
% 信噪比/dB SLSTM SGRU BiLSTM BiGRU Attention-BiGRU CNN-BiGRU AF-BiTCN −10 56.69 56.08 54.00 56.11 56.22 55.58 56.88 −5 66.02 66.00 67.67 68.25 67.81 66.56 69.22 0 75.86 75.61 74.83 75.55 77.02 75.33 77.21 5 82.83 81.61 81.00 82.22 83.13 82.94 83.64 10 86.33 86.64 84.78 86.05 87.50 87.89 89.03 20 89.91 91.86 88.33 90.97 91.61 92.36 92.45
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表 4 不同融合方法的性能对比
Table 4. Performance comparison of different fusion methods
% 融合方法 弹道目标类别 准确率 精确率 召回率 F1分数 加性融合 弹头 91.60 92.74 91.74 92.24 高仿诱饵 95.92 93.04 95.91 94.46 简单诱饵 97.33 98.40 97.25 97.82 球形诱饵 100.00 100.00 100.00 100.00 母舱 98.08 98.90 98.08 98.49 同维度连接 弹头 89.75 93.24 89.82 91.50 高仿诱饵 94.33 93.16 94.33 93.74 简单诱饵 98.58 94.94 98.50 96.68 球形诱饵 100.00 100.00 100.00 100.00 母舱 97.92 99.23 97.91 98.57 乘性融合 弹头 87.42 95.01 87.48 91.09 高仿诱饵 95.75 92.28 95.75 93.98 简单诱饵 99.33 93.27 99.25 96.16 球形诱饵 100.00 100.00 100.00 100.00 母舱 97.58 99.82 97.58 98.69
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表 5 AF-BiTCN消融实验结果
Table 5. Ablation experiments results of AF-BiTCN
% 模型 准确率 召回率 精确率 F1分数 AF-BiTCN 96.60 96.62 96.60 96.60 正向TCN 96.00 96.00 96.01 95.98 反向TCN 96.33 96.33 96.38 96.31
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